Companies that receive large volumes of telephone calls or emails, or that operate at all hours, often employ computer-implemented customer service user interfaces to provide a consistent response to customers, minimize wait times, and minimize staffing costs.
One form of such a computer-implemented customer service user interface is an interactive voice response (IVR) system, which uses a computer to provide an interface with external customers, whereby the customers are able to navigate a menu provided by the IVR system through the use of voice recognition and/or Dual-Tone Multi-Frequency (DTMF) tones input through a telephone keypad, or the like. Companies may program the IVR systems with various states and flows among states, based on expected user responses. IVR systems are often employed for banking payments and services, retail orders, utilities, travel information, and weather conditions.
Another form or computer-implemented user interface is a chatbot, which utilizes artificial intelligence to conduct a conversation with a customer, such as via a textual exchange conducted in a display region of a webpage.
Existing customer user interface systems are relatively rigid, providing customers with a fixed set of options from which to choose. Some systems use question-answering machines, which are programmed to answer questions posed by humans in a natural language. Most natural language question-answering machines are rule-based systems composed of two types of rules: (i) “matching rules”, which match natural language inputs to a response; and (ii) “response rules”, which then react to this natural language. Both matching rules and response rules must be coded up in advance, being pre-programmed into a system, for the system to react meaningfully during real-time execution.
Other learning systems use a supervised learning approach. A machine is trained with a number of questions and corresponding sets of expected answers, strengthening the links between the best matches of questions and answers and weakening the links between incorrect questions and answers. Such a supervised learning approach requires a large amount of both questions and answers before results can be reasonably ascertained.
Thus, a need exists to provide an improved method and system for answering natural language questions in computer-implemented user interface systems.